Overview

Brought to you by YData

Dataset statistics

Number of variables22
Number of observations2126
Missing cells2244
Missing cells (%)4.8%
Duplicate rows2
Duplicate rows (%)0.1%
Total size in memory365.5 KiB
Average record size in memory176.1 B

Variable types

Numeric20
Categorical2

Alerts

Dataset has 2 (0.1%) duplicate rowsDuplicates
ALTV is highly overall correlated with MSTV and 2 other fieldsHigh correlation
ASTV is highly overall correlated with MSTVHigh correlation
DL is highly overall correlated with MSTV and 3 other fieldsHigh correlation
DS is highly overall correlated with Median and 1 other fieldsHigh correlation
LB is highly overall correlated with Mean and 2 other fieldsHigh correlation
MSTV is highly overall correlated with ALTV and 6 other fieldsHigh correlation
Max is highly overall correlated with Nmax and 2 other fieldsHigh correlation
Mean is highly overall correlated with LB and 2 other fieldsHigh correlation
Median is highly overall correlated with DS and 3 other fieldsHigh correlation
Min is highly overall correlated with DL and 4 other fieldsHigh correlation
Mode is highly overall correlated with DS and 3 other fieldsHigh correlation
Nmax is highly overall correlated with MSTV and 4 other fieldsHigh correlation
Variance is highly overall correlated with ALTV and 6 other fieldsHigh correlation
Width is highly overall correlated with ALTV and 6 other fieldsHigh correlation
DS is highly imbalanced (97.0%) Imbalance
LB has 103 (4.8%) missing values Missing
AC has 113 (5.3%) missing values Missing
FM has 109 (5.1%) missing values Missing
UC has 114 (5.4%) missing values Missing
DL has 85 (4.0%) missing values Missing
DS has 132 (6.2%) missing values Missing
DP has 110 (5.2%) missing values Missing
ASTV has 111 (5.2%) missing values Missing
MSTV has 102 (4.8%) missing values Missing
ALTV has 96 (4.5%) missing values Missing
MLTV has 109 (5.1%) missing values Missing
Width has 103 (4.8%) missing values Missing
Min has 126 (5.9%) missing values Missing
Max has 88 (4.1%) missing values Missing
Nmax has 105 (4.9%) missing values Missing
Nzeros has 124 (5.8%) missing values Missing
Mode has 102 (4.8%) missing values Missing
Mean has 107 (5.0%) missing values Missing
Median has 101 (4.8%) missing values Missing
Variance has 97 (4.6%) missing values Missing
Tendency has 107 (5.0%) missing values Missing
AC has 852 (40.1%) zeros Zeros
FM has 1243 (58.5%) zeros Zeros
UC has 313 (14.7%) zeros Zeros
DL has 1186 (55.8%) zeros Zeros
DP has 1844 (86.7%) zeros Zeros
ALTV has 1184 (55.7%) zeros Zeros
MLTV has 125 (5.9%) zeros Zeros
Nmax has 99 (4.7%) zeros Zeros
Nzeros has 1529 (71.9%) zeros Zeros
Variance has 174 (8.2%) zeros Zeros

Reproduction

Analysis started2025-05-29 13:00:37.192904
Analysis finished2025-05-29 13:00:58.179772
Duration20.99 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

LB
Real number (ℝ)

High correlation  Missing 

Distinct48
Distinct (%)2.4%
Missing103
Missing (%)4.8%
Infinite0
Infinite (%)0.0%
Mean133.27682
Minimum106
Maximum160
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2025-05-29T15:00:58.223655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum106
5-th percentile119
Q1126
median133
Q3140
95-th percentile149
Maximum160
Range54
Interquartile range (IQR)14

Descriptive statistics

Standard deviation9.8115523
Coefficient of variation (CV)0.073617847
Kurtosis-0.31837122
Mean133.27682
Median Absolute Deviation (MAD)7
Skewness0.0086287892
Sum269619
Variance96.266559
MonotonicityNot monotonic
2025-05-29T15:00:58.289479image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
133 126
 
5.9%
130 106
 
5.0%
138 99
 
4.7%
122 99
 
4.7%
125 90
 
4.2%
128 80
 
3.8%
120 78
 
3.7%
144 74
 
3.5%
142 73
 
3.4%
132 70
 
3.3%
Other values (38) 1128
53.1%
(Missing) 103
 
4.8%
ValueCountFrequency (%)
106 7
 
0.3%
110 19
 
0.9%
112 15
 
0.7%
114 11
 
0.5%
115 26
 
1.2%
116 5
 
0.2%
117 2
 
0.1%
118 9
 
0.4%
119 16
 
0.8%
120 78
3.7%
ValueCountFrequency (%)
160 1
 
< 0.1%
159 9
 
0.4%
158 9
 
0.4%
157 4
 
0.2%
156 4
 
0.2%
154 7
 
0.3%
152 16
0.8%
151 14
0.7%
150 25
1.2%
149 18
0.8%

AC
Real number (ℝ)

Missing  Zeros 

Distinct19
Distinct (%)0.9%
Missing113
Missing (%)5.3%
Infinite0
Infinite (%)0.0%
Mean0.0031485345
Minimum0
Maximum0.018
Zeros852
Zeros (%)40.1%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2025-05-29T15:00:58.342232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.002
Q30.006
95-th percentile0.011
Maximum0.018
Range0.018
Interquartile range (IQR)0.006

Descriptive statistics

Standard deviation0.0038380866
Coefficient of variation (CV)1.2190073
Kurtosis0.66925024
Mean0.0031485345
Median Absolute Deviation (MAD)0.002
Skewness1.1911336
Sum6.338
Variance1.4730909 × 10-5
MonotonicityNot monotonic
2025-05-29T15:00:58.391104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 852
40.1%
0.003 151
 
7.1%
0.002 151
 
7.1%
0.001 136
 
6.4%
0.004 114
 
5.4%
0.006 107
 
5.0%
0.005 101
 
4.8%
0.008 97
 
4.6%
0.007 85
 
4.0%
0.009 52
 
2.4%
Other values (9) 167
 
7.9%
(Missing) 113
 
5.3%
ValueCountFrequency (%)
0 852
40.1%
0.001 136
 
6.4%
0.002 151
 
7.1%
0.003 151
 
7.1%
0.004 114
 
5.4%
0.005 101
 
4.8%
0.006 107
 
5.0%
0.007 85
 
4.0%
0.008 97
 
4.6%
0.009 52
 
2.4%
ValueCountFrequency (%)
0.018 1
 
< 0.1%
0.017 3
 
0.1%
0.016 7
 
0.3%
0.015 9
 
0.4%
0.014 20
 
0.9%
0.013 19
 
0.9%
0.012 24
1.1%
0.011 34
1.6%
0.01 50
2.4%
0.009 52
2.4%

FM
Real number (ℝ)

Missing  Zeros 

Distinct99
Distinct (%)4.9%
Missing109
Missing (%)5.1%
Infinite0
Infinite (%)0.0%
Mean0.009224591
Minimum0
Maximum0.481
Zeros1243
Zeros (%)58.5%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2025-05-29T15:00:58.453459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.003
95-th percentile0.029
Maximum0.481
Range0.481
Interquartile range (IQR)0.003

Descriptive statistics

Standard deviation0.045050155
Coefficient of variation (CV)4.8837022
Kurtosis67.509358
Mean0.009224591
Median Absolute Deviation (MAD)0
Skewness7.9711795
Sum18.606
Variance0.0020295165
MonotonicityNot monotonic
2025-05-29T15:00:58.524269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1243
58.5%
0.001 151
 
7.1%
0.002 108
 
5.1%
0.003 81
 
3.8%
0.004 48
 
2.3%
0.005 34
 
1.6%
0.006 30
 
1.4%
0.007 27
 
1.3%
0.009 24
 
1.1%
0.01 24
 
1.1%
Other values (89) 247
 
11.6%
(Missing) 109
 
5.1%
ValueCountFrequency (%)
0 1243
58.5%
0.001 151
 
7.1%
0.002 108
 
5.1%
0.003 81
 
3.8%
0.004 48
 
2.3%
0.005 34
 
1.6%
0.006 30
 
1.4%
0.007 27
 
1.3%
0.008 23
 
1.1%
0.009 24
 
1.1%
ValueCountFrequency (%)
0.481 1
< 0.1%
0.477 1
< 0.1%
0.469 1
< 0.1%
0.455 1
< 0.1%
0.451 1
< 0.1%
0.446 1
< 0.1%
0.443 1
< 0.1%
0.441 1
< 0.1%
0.43 1
< 0.1%
0.408 2
0.1%

UC
Real number (ℝ)

Missing  Zeros 

Distinct16
Distinct (%)0.8%
Missing114
Missing (%)5.4%
Infinite0
Infinite (%)0.0%
Mean0.0043841948
Minimum0
Maximum0.015
Zeros313
Zeros (%)14.7%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2025-05-29T15:00:58.577686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.002
median0.005
Q30.007
95-th percentile0.009
Maximum0.015
Range0.015
Interquartile range (IQR)0.005

Descriptive statistics

Standard deviation0.0029521307
Coefficient of variation (CV)0.67335756
Kurtosis-0.64030731
Mean0.0043841948
Median Absolute Deviation (MAD)0.002
Skewness0.15208048
Sum8.821
Variance8.7150758 × 10-6
MonotonicityNot monotonic
2025-05-29T15:00:58.627557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 313
14.7%
0.005 276
13.0%
0.004 229
10.8%
0.006 216
10.2%
0.007 209
9.8%
0.003 199
9.4%
0.008 154
7.2%
0.002 148
7.0%
0.001 113
 
5.3%
0.009 76
 
3.6%
Other values (6) 79
 
3.7%
(Missing) 114
 
5.4%
ValueCountFrequency (%)
0 313
14.7%
0.001 113
 
5.3%
0.002 148
7.0%
0.003 199
9.4%
0.004 229
10.8%
0.005 276
13.0%
0.006 216
10.2%
0.007 209
9.8%
0.008 154
7.2%
0.009 76
 
3.6%
ValueCountFrequency (%)
0.015 1
 
< 0.1%
0.014 2
 
0.1%
0.013 2
 
0.1%
0.012 10
 
0.5%
0.011 15
 
0.7%
0.01 49
 
2.3%
0.009 76
 
3.6%
0.008 154
7.2%
0.007 209
9.8%
0.006 216
10.2%

DL
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct16
Distinct (%)0.8%
Missing85
Missing (%)4.0%
Infinite0
Infinite (%)0.0%
Mean0.0018853503
Minimum0
Maximum0.015
Zeros1186
Zeros (%)55.8%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2025-05-29T15:00:58.676004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.003
95-th percentile0.008
Maximum0.015
Range0.015
Interquartile range (IQR)0.003

Descriptive statistics

Standard deviation0.002954655
Coefficient of variation (CV)1.567165
Kurtosis2.4550414
Mean0.0018853503
Median Absolute Deviation (MAD)0
Skewness1.7066247
Sum3.848
Variance8.7299863 × 10-6
MonotonicityNot monotonic
2025-05-29T15:00:58.725234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 1186
55.8%
0.001 154
 
7.2%
0.004 111
 
5.2%
0.003 110
 
5.2%
0.002 110
 
5.2%
0.005 101
 
4.8%
0.006 73
 
3.4%
0.008 53
 
2.5%
0.007 52
 
2.4%
0.009 37
 
1.7%
Other values (6) 54
 
2.5%
(Missing) 85
 
4.0%
ValueCountFrequency (%)
0 1186
55.8%
0.001 154
 
7.2%
0.002 110
 
5.2%
0.003 110
 
5.2%
0.004 111
 
5.2%
0.005 101
 
4.8%
0.006 73
 
3.4%
0.007 52
 
2.4%
0.008 53
 
2.5%
0.009 37
 
1.7%
ValueCountFrequency (%)
0.015 3
 
0.1%
0.014 6
 
0.3%
0.013 7
 
0.3%
0.012 12
 
0.6%
0.011 12
 
0.6%
0.01 14
 
0.7%
0.009 37
1.7%
0.008 53
2.5%
0.007 52
2.4%
0.006 73
3.4%

DS
Categorical

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)0.1%
Missing132
Missing (%)6.2%
Memory size16.7 KiB
0.0
1988 
0.001
 
6

Length

Max length5
Median length3
Mean length3.0060181
Min length3

Characters and Unicode

Total characters5994
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1988
93.5%
0.001 6
 
0.3%
(Missing) 132
 
6.2%

Length

2025-05-29T15:00:58.784543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-29T15:00:58.823615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1988
99.7%
0.001 6
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 3994
66.6%
. 1994
33.3%
1 6
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5994
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3994
66.6%
. 1994
33.3%
1 6
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5994
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3994
66.6%
. 1994
33.3%
1 6
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5994
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3994
66.6%
. 1994
33.3%
1 6
 
0.1%

DP
Real number (ℝ)

Missing  Zeros 

Distinct6
Distinct (%)0.3%
Missing110
Missing (%)5.2%
Infinite0
Infinite (%)0.0%
Mean0.00016319444
Minimum0
Maximum0.005
Zeros1844
Zeros (%)86.7%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2025-05-29T15:00:58.855054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.002
Maximum0.005
Range0.005
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.00060078899
Coefficient of variation (CV)3.6814304
Kurtosis19.831489
Mean0.00016319444
Median Absolute Deviation (MAD)0
Skewness4.2612824
Sum0.329
Variance3.6094741 × 10-7
MonotonicityNot monotonic
2025-05-29T15:00:58.898311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 1844
86.7%
0.002 70
 
3.3%
0.001 66
 
3.1%
0.003 24
 
1.1%
0.004 9
 
0.4%
0.005 3
 
0.1%
(Missing) 110
 
5.2%
ValueCountFrequency (%)
0 1844
86.7%
0.001 66
 
3.1%
0.002 70
 
3.3%
0.003 24
 
1.1%
0.004 9
 
0.4%
0.005 3
 
0.1%
ValueCountFrequency (%)
0.005 3
 
0.1%
0.004 9
 
0.4%
0.003 24
 
1.1%
0.002 70
 
3.3%
0.001 66
 
3.1%
0 1844
86.7%

ASTV
Real number (ℝ)

High correlation  Missing 

Distinct75
Distinct (%)3.7%
Missing111
Missing (%)5.2%
Infinite0
Infinite (%)0.0%
Mean47.043176
Minimum12
Maximum87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2025-05-29T15:00:58.952819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile21
Q132
median49
Q361
95-th percentile75
Maximum87
Range75
Interquartile range (IQR)29

Descriptive statistics

Standard deviation17.211507
Coefficient of variation (CV)0.36586617
Kurtosis-1.039197
Mean47.043176
Median Absolute Deviation (MAD)14
Skewness-0.0086523539
Sum94792
Variance296.23597
MonotonicityNot monotonic
2025-05-29T15:00:59.020444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
64 58
 
2.7%
60 57
 
2.7%
63 56
 
2.6%
58 55
 
2.6%
61 55
 
2.6%
65 55
 
2.6%
51 52
 
2.4%
62 50
 
2.4%
22 45
 
2.1%
37 42
 
2.0%
Other values (65) 1490
70.1%
(Missing) 111
 
5.2%
ValueCountFrequency (%)
12 2
 
0.1%
13 7
 
0.3%
14 4
 
0.2%
15 4
 
0.2%
16 12
 
0.6%
17 11
 
0.5%
18 9
 
0.4%
19 18
0.8%
20 26
1.2%
21 32
1.5%
ValueCountFrequency (%)
87 1
 
< 0.1%
86 4
 
0.2%
84 6
 
0.3%
83 4
 
0.2%
82 2
 
0.1%
81 7
 
0.3%
80 7
 
0.3%
79 14
0.7%
78 19
0.9%
77 16
0.8%

MSTV
Real number (ℝ)

High correlation  Missing 

Distinct57
Distinct (%)2.8%
Missing102
Missing (%)4.8%
Infinite0
Infinite (%)0.0%
Mean1.3313241
Minimum0.2
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2025-05-29T15:00:59.086524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.3
Q10.7
median1.2
Q31.7
95-th percentile3
Maximum7
Range6.8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8834934
Coefficient of variation (CV)0.66362007
Kurtosis4.8290962
Mean1.3313241
Median Absolute Deviation (MAD)0.5
Skewness1.675154
Sum2694.6
Variance0.78056058
MonotonicityNot monotonic
2025-05-29T15:00:59.210214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8 119
 
5.6%
1.3 115
 
5.4%
0.5 114
 
5.4%
0.7 113
 
5.3%
0.4 113
 
5.3%
0.9 109
 
5.1%
0.6 109
 
5.1%
1.2 100
 
4.7%
1.5 97
 
4.6%
1 95
 
4.5%
Other values (47) 940
44.2%
(Missing) 102
 
4.8%
ValueCountFrequency (%)
0.2 45
 
2.1%
0.3 81
3.8%
0.4 113
5.3%
0.5 114
5.4%
0.6 109
5.1%
0.7 113
5.3%
0.8 119
5.6%
0.9 109
5.1%
1 95
4.5%
1.1 91
4.3%
ValueCountFrequency (%)
7 1
< 0.1%
6.9 1
< 0.1%
6.3 2
0.1%
6 1
< 0.1%
5.9 1
< 0.1%
5.7 1
< 0.1%
5.4 2
0.1%
5.3 1
< 0.1%
5.2 1
< 0.1%
5 1
< 0.1%

ALTV
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct87
Distinct (%)4.3%
Missing96
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean9.8596059
Minimum0
Maximum91
Zeros1184
Zeros (%)55.7%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2025-05-29T15:00:59.275059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q311
95-th percentile56
Maximum91
Range91
Interquartile range (IQR)11

Descriptive statistics

Standard deviation18.418215
Coefficient of variation (CV)1.8680478
Kurtosis4.2804351
Mean9.8596059
Median Absolute Deviation (MAD)0
Skewness2.199303
Sum20015
Variance339.23065
MonotonicityNot monotonic
2025-05-29T15:00:59.340225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1184
55.7%
1 50
 
2.4%
5 42
 
2.0%
2 41
 
1.9%
4 38
 
1.8%
3 33
 
1.6%
8 32
 
1.5%
6 31
 
1.5%
12 28
 
1.3%
7 22
 
1.0%
Other values (77) 529
24.9%
(Missing) 96
 
4.5%
ValueCountFrequency (%)
0 1184
55.7%
1 50
 
2.4%
2 41
 
1.9%
3 33
 
1.6%
4 38
 
1.8%
5 42
 
2.0%
6 31
 
1.5%
7 22
 
1.0%
8 32
 
1.5%
9 21
 
1.0%
ValueCountFrequency (%)
91 4
0.2%
90 2
 
0.1%
88 1
 
< 0.1%
86 1
 
< 0.1%
85 1
 
< 0.1%
84 6
0.3%
82 1
 
< 0.1%
81 1
 
< 0.1%
79 1
 
< 0.1%
78 3
0.1%

MLTV
Real number (ℝ)

Missing  Zeros 

Distinct246
Distinct (%)12.2%
Missing109
Missing (%)5.1%
Infinite0
Infinite (%)0.0%
Mean8.2092712
Minimum0
Maximum50.7
Zeros125
Zeros (%)5.9%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2025-05-29T15:00:59.403227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.6
median7.4
Q310.8
95-th percentile18.42
Maximum50.7
Range50.7
Interquartile range (IQR)6.2

Descriptive statistics

Standard deviation5.6116136
Coefficient of variation (CV)0.68357025
Kurtosis4.3339432
Mean8.2092712
Median Absolute Deviation (MAD)3.1
Skewness1.3616317
Sum16558.1
Variance31.490207
MonotonicityNot monotonic
2025-05-29T15:00:59.465269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 125
 
5.9%
7.1 28
 
1.3%
6.7 25
 
1.2%
5.2 24
 
1.1%
6.8 23
 
1.1%
6.5 23
 
1.1%
9.5 23
 
1.1%
5.6 23
 
1.1%
7.2 22
 
1.0%
4.9 21
 
1.0%
Other values (236) 1680
79.0%
(Missing) 109
 
5.1%
ValueCountFrequency (%)
0 125
5.9%
0.1 4
 
0.2%
0.2 3
 
0.1%
0.3 7
 
0.3%
0.4 6
 
0.3%
0.5 11
 
0.5%
0.6 3
 
0.1%
0.7 4
 
0.2%
0.9 5
 
0.2%
1 3
 
0.1%
ValueCountFrequency (%)
50.7 1
< 0.1%
41.8 1
< 0.1%
40.8 1
< 0.1%
36.9 1
< 0.1%
35.7 1
< 0.1%
34.7 1
< 0.1%
33.5 1
< 0.1%
29.5 1
< 0.1%
29.3 1
< 0.1%
29.1 1
< 0.1%

Width
Real number (ℝ)

High correlation  Missing 

Distinct154
Distinct (%)7.6%
Missing103
Missing (%)4.8%
Infinite0
Infinite (%)0.0%
Mean70.796342
Minimum3
Maximum180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2025-05-29T15:00:59.525331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile16.1
Q137
median68
Q3101
95-th percentile138
Maximum180
Range177
Interquartile range (IQR)64

Descriptive statistics

Standard deviation39.056403
Coefficient of variation (CV)0.55167261
Kurtosis-0.91150181
Mean70.796342
Median Absolute Deviation (MAD)32
Skewness0.30786514
Sum143221
Variance1525.4026
MonotonicityNot monotonic
2025-05-29T15:00:59.590787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39 41
 
1.9%
102 35
 
1.6%
31 28
 
1.3%
27 27
 
1.3%
22 27
 
1.3%
90 27
 
1.3%
98 27
 
1.3%
96 26
 
1.2%
20 25
 
1.2%
42 25
 
1.2%
Other values (144) 1735
81.6%
(Missing) 103
 
4.8%
ValueCountFrequency (%)
3 1
 
< 0.1%
5 2
 
0.1%
6 1
 
< 0.1%
7 3
 
0.1%
8 10
0.5%
9 5
 
0.2%
10 7
 
0.3%
11 9
0.4%
12 20
0.9%
13 13
0.6%
ValueCountFrequency (%)
180 1
 
< 0.1%
176 6
0.3%
163 2
 
0.1%
162 1
 
< 0.1%
161 5
0.2%
158 2
 
0.1%
153 3
 
0.1%
150 10
0.5%
149 11
0.5%
148 8
0.4%

Min
Real number (ℝ)

High correlation  Missing 

Distinct109
Distinct (%)5.5%
Missing126
Missing (%)5.9%
Infinite0
Infinite (%)0.0%
Mean93.4115
Minimum50
Maximum159
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2025-05-29T15:00:59.657953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile51
Q167
median93
Q3120
95-th percentile139
Maximum159
Range109
Interquartile range (IQR)53

Descriptive statistics

Standard deviation29.553915
Coefficient of variation (CV)0.31638412
Kurtosis-1.2841808
Mean93.4115
Median Absolute Deviation (MAD)27
Skewness0.12592899
Sum186823
Variance873.43388
MonotonicityNot monotonic
2025-05-29T15:00:59.726683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 73
 
3.4%
52 47
 
2.2%
71 46
 
2.2%
60 43
 
2.0%
120 43
 
2.0%
68 40
 
1.9%
67 40
 
1.9%
103 38
 
1.8%
51 35
 
1.6%
62 34
 
1.6%
Other values (99) 1561
73.4%
(Missing) 126
 
5.9%
ValueCountFrequency (%)
50 73
3.4%
51 35
1.6%
52 47
2.2%
53 32
1.5%
54 25
 
1.2%
55 16
 
0.8%
56 19
 
0.9%
57 21
 
1.0%
58 21
 
1.0%
59 14
 
0.7%
ValueCountFrequency (%)
159 1
 
< 0.1%
158 1
 
< 0.1%
156 1
 
< 0.1%
155 2
 
0.1%
154 3
0.1%
153 7
0.3%
152 4
0.2%
151 3
0.1%
150 3
0.1%
149 2
 
0.1%

Max
Real number (ℝ)

High correlation  Missing 

Distinct86
Distinct (%)4.2%
Missing88
Missing (%)4.1%
Infinite0
Infinite (%)0.0%
Mean164.06477
Minimum122
Maximum238
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2025-05-29T15:00:59.792032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum122
5-th percentile138
Q1152
median162
Q3174
95-th percentile198
Maximum238
Range116
Interquartile range (IQR)22

Descriptive statistics

Standard deviation18.012616
Coefficient of variation (CV)0.10978966
Kurtosis0.65400444
Mean164.06477
Median Absolute Deviation (MAD)11
Skewness0.59098757
Sum334364
Variance324.45432
MonotonicityNot monotonic
2025-05-29T15:00:59.860214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
157 70
 
3.3%
171 63
 
3.0%
158 61
 
2.9%
159 58
 
2.7%
156 54
 
2.5%
152 52
 
2.4%
154 51
 
2.4%
178 49
 
2.3%
153 47
 
2.2%
172 46
 
2.2%
Other values (76) 1487
69.9%
(Missing) 88
 
4.1%
ValueCountFrequency (%)
122 2
 
0.1%
123 2
 
0.1%
125 2
 
0.1%
126 5
0.2%
127 2
 
0.1%
128 4
0.2%
129 9
0.4%
130 8
0.4%
131 7
0.3%
132 3
 
0.1%
ValueCountFrequency (%)
238 6
 
0.3%
230 3
 
0.1%
228 5
 
0.2%
213 1
 
< 0.1%
211 5
 
0.2%
210 4
 
0.2%
205 1
 
< 0.1%
204 3
 
0.1%
200 30
1.4%
199 19
0.9%

Nmax
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct18
Distinct (%)0.9%
Missing105
Missing (%)4.9%
Infinite0
Infinite (%)0.0%
Mean4.0905492
Minimum0
Maximum18
Zeros99
Zeros (%)4.7%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2025-05-29T15:00:59.913175image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q36
95-th percentile10
Maximum18
Range18
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.9554807
Coefficient of variation (CV)0.72251439
Kurtosis0.52491569
Mean4.0905492
Median Absolute Deviation (MAD)2
Skewness0.89631226
Sum8267
Variance8.7348661
MonotonicityNot monotonic
2025-05-29T15:00:59.959193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1 335
15.8%
2 314
14.8%
3 260
12.2%
4 244
11.5%
5 199
9.4%
6 150
7.1%
7 141
6.6%
8 104
 
4.9%
0 99
 
4.7%
9 61
 
2.9%
Other values (8) 114
 
5.4%
(Missing) 105
 
4.9%
ValueCountFrequency (%)
0 99
 
4.7%
1 335
15.8%
2 314
14.8%
3 260
12.2%
4 244
11.5%
5 199
9.4%
6 150
7.1%
7 141
6.6%
8 104
 
4.9%
9 61
 
2.9%
ValueCountFrequency (%)
18 1
 
< 0.1%
16 2
 
0.1%
15 1
 
< 0.1%
14 5
 
0.2%
13 10
 
0.5%
12 22
 
1.0%
11 26
 
1.2%
10 47
2.2%
9 61
2.9%
8 104
4.9%

Nzeros
Real number (ℝ)

Missing  Zeros 

Distinct7
Distinct (%)0.3%
Missing124
Missing (%)5.8%
Infinite0
Infinite (%)0.0%
Mean0.31868132
Minimum0
Maximum10
Zeros1529
Zeros (%)71.9%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2025-05-29T15:01:00.000596image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.68198835
Coefficient of variation (CV)2.1400324
Kurtosis28.687955
Mean0.31868132
Median Absolute Deviation (MAD)0
Skewness3.692649
Sum638
Variance0.46510811
MonotonicityNot monotonic
2025-05-29T15:01:00.044950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 1529
71.9%
1 347
 
16.3%
2 102
 
4.8%
3 20
 
0.9%
5 2
 
0.1%
10 1
 
< 0.1%
7 1
 
< 0.1%
(Missing) 124
 
5.8%
ValueCountFrequency (%)
0 1529
71.9%
1 347
 
16.3%
2 102
 
4.8%
3 20
 
0.9%
5 2
 
0.1%
7 1
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
7 1
 
< 0.1%
5 2
 
0.1%
3 20
 
0.9%
2 102
 
4.8%
1 347
 
16.3%
0 1529
71.9%

Mode
Real number (ℝ)

High correlation  Missing 

Distinct88
Distinct (%)4.3%
Missing102
Missing (%)4.8%
Infinite0
Infinite (%)0.0%
Mean137.35326
Minimum60
Maximum187
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2025-05-29T15:01:00.101453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile111.15
Q1129
median139
Q3148
95-th percentile160
Maximum187
Range127
Interquartile range (IQR)19

Descriptive statistics

Standard deviation16.432435
Coefficient of variation (CV)0.11963629
Kurtosis3.0637548
Mean137.35326
Median Absolute Deviation (MAD)10
Skewness-1.0064362
Sum278003
Variance270.02492
MonotonicityNot monotonic
2025-05-29T15:01:00.227749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
133 129
 
6.1%
142 85
 
4.0%
136 84
 
4.0%
150 80
 
3.8%
144 77
 
3.6%
148 77
 
3.6%
129 71
 
3.3%
143 70
 
3.3%
126 65
 
3.1%
125 64
 
3.0%
Other values (78) 1222
57.5%
(Missing) 102
 
4.8%
ValueCountFrequency (%)
60 6
0.3%
67 5
0.2%
69 1
 
< 0.1%
71 1
 
< 0.1%
75 6
0.3%
76 1
 
< 0.1%
77 1
 
< 0.1%
86 10
0.5%
88 6
0.3%
89 3
 
0.1%
ValueCountFrequency (%)
187 1
 
< 0.1%
186 6
0.3%
180 4
0.2%
179 1
 
< 0.1%
176 5
0.2%
170 4
0.2%
169 3
 
0.1%
167 8
0.4%
165 9
0.4%
164 1
 
< 0.1%

Mean
Real number (ℝ)

High correlation  Missing 

Distinct102
Distinct (%)5.1%
Missing107
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean134.61714
Minimum73
Maximum182
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2025-05-29T15:01:00.295065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum73
5-th percentile108
Q1125
median136
Q3145
95-th percentile157
Maximum182
Range109
Interquartile range (IQR)20

Descriptive statistics

Standard deviation15.608393
Coefficient of variation (CV)0.11594655
Kurtosis0.95196748
Mean134.61714
Median Absolute Deviation (MAD)10
Skewness-0.64465983
Sum271792
Variance243.62193
MonotonicityNot monotonic
2025-05-29T15:01:00.363221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
143 63
 
3.0%
135 62
 
2.9%
144 61
 
2.9%
140 58
 
2.7%
141 58
 
2.7%
147 56
 
2.6%
132 56
 
2.6%
125 54
 
2.5%
136 53
 
2.5%
134 52
 
2.4%
Other values (92) 1446
68.0%
(Missing) 107
 
5.0%
ValueCountFrequency (%)
73 1
 
< 0.1%
75 1
 
< 0.1%
76 1
 
< 0.1%
78 1
 
< 0.1%
79 1
 
< 0.1%
80 2
0.1%
81 1
 
< 0.1%
82 2
0.1%
83 4
0.2%
84 3
0.1%
ValueCountFrequency (%)
182 1
< 0.1%
180 1
< 0.1%
178 1
< 0.1%
175 1
< 0.1%
173 2
0.1%
172 1
< 0.1%
171 2
0.1%
170 1
< 0.1%
169 1
< 0.1%
168 1
< 0.1%

Median
Real number (ℝ)

High correlation  Missing 

Distinct93
Distinct (%)4.6%
Missing101
Missing (%)4.8%
Infinite0
Infinite (%)0.0%
Mean138.22074
Minimum77
Maximum186
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2025-05-29T15:01:00.430259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum77
5-th percentile113
Q1129
median140
Q3148
95-th percentile159
Maximum186
Range109
Interquartile range (IQR)19

Descriptive statistics

Standard deviation14.393611
Coefficient of variation (CV)0.10413496
Kurtosis0.60427983
Mean138.22074
Median Absolute Deviation (MAD)9
Skewness-0.46265533
Sum279897
Variance207.17605
MonotonicityNot monotonic
2025-05-29T15:01:00.497882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
146 67
 
3.2%
142 66
 
3.1%
145 65
 
3.1%
147 64
 
3.0%
137 64
 
3.0%
141 61
 
2.9%
151 61
 
2.9%
134 59
 
2.8%
149 59
 
2.8%
143 55
 
2.6%
Other values (83) 1404
66.0%
(Missing) 101
 
4.8%
ValueCountFrequency (%)
77 1
 
< 0.1%
79 2
0.1%
82 1
 
< 0.1%
87 1
 
< 0.1%
90 1
 
< 0.1%
91 1
 
< 0.1%
92 2
0.1%
93 1
 
< 0.1%
94 2
0.1%
95 4
0.2%
ValueCountFrequency (%)
186 1
< 0.1%
183 1
< 0.1%
180 1
< 0.1%
178 1
< 0.1%
177 1
< 0.1%
176 2
0.1%
174 2
0.1%
172 1
< 0.1%
171 1
< 0.1%
170 2
0.1%

Variance
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct132
Distinct (%)6.5%
Missing97
Missing (%)4.6%
Infinite0
Infinite (%)0.0%
Mean19.130113
Minimum0
Maximum269
Zeros174
Zeros (%)8.2%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2025-05-29T15:01:00.564308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median8
Q324
95-th percentile77
Maximum269
Range269
Interquartile range (IQR)22

Descriptive statistics

Standard deviation29.320356
Coefficient of variation (CV)1.5326807
Kurtosis14.878963
Mean19.130113
Median Absolute Deviation (MAD)7
Skewness3.1958993
Sum38815
Variance859.68326
MonotonicityNot monotonic
2025-05-29T15:01:00.629673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 235
 
11.1%
0 174
 
8.2%
2 156
 
7.3%
3 150
 
7.1%
4 104
 
4.9%
5 83
 
3.9%
8 71
 
3.3%
6 61
 
2.9%
7 50
 
2.4%
9 48
 
2.3%
Other values (122) 897
42.2%
(Missing) 97
 
4.6%
ValueCountFrequency (%)
0 174
8.2%
1 235
11.1%
2 156
7.3%
3 150
7.1%
4 104
4.9%
5 83
 
3.9%
6 61
 
2.9%
7 50
 
2.4%
8 71
 
3.3%
9 48
 
2.3%
ValueCountFrequency (%)
269 1
< 0.1%
254 1
< 0.1%
250 1
< 0.1%
243 1
< 0.1%
241 1
< 0.1%
215 1
< 0.1%
195 1
< 0.1%
190 1
< 0.1%
182 1
< 0.1%
177 1
< 0.1%

Tendency
Categorical

Missing 

Distinct3
Distinct (%)0.1%
Missing107
Missing (%)5.0%
Memory size16.7 KiB
0.0
1057 
1.0
803 
-1.0
159 

Length

Max length4
Median length3
Mean length3.0787519
Min length3

Characters and Unicode

Total characters6216
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1057
49.7%
1.0 803
37.8%
-1.0 159
 
7.5%
(Missing) 107
 
5.0%

Length

2025-05-29T15:01:00.691046image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-29T15:01:00.728296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1057
52.4%
1.0 962
47.6%

Most occurring characters

ValueCountFrequency (%)
0 3076
49.5%
. 2019
32.5%
1 962
 
15.5%
- 159
 
2.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6216
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3076
49.5%
. 2019
32.5%
1 962
 
15.5%
- 159
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6216
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3076
49.5%
. 2019
32.5%
1 962
 
15.5%
- 159
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6216
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3076
49.5%
. 2019
32.5%
1 962
 
15.5%
- 159
 
2.6%

CLASS
Real number (ℝ)

Distinct10
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5098777
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2025-05-29T15:01:00.763708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q37
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.0268829
Coefficient of variation (CV)0.6711674
Kurtosis-1.2290404
Mean4.5098777
Median Absolute Deviation (MAD)2
Skewness0.38116341
Sum9588
Variance9.16202
MonotonicityNot monotonic
2025-05-29T15:01:00.806105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2 579
27.2%
1 384
18.1%
6 332
15.6%
7 252
11.9%
10 197
 
9.3%
8 107
 
5.0%
4 81
 
3.8%
5 72
 
3.4%
9 69
 
3.2%
3 53
 
2.5%
ValueCountFrequency (%)
1 384
18.1%
2 579
27.2%
3 53
 
2.5%
4 81
 
3.8%
5 72
 
3.4%
6 332
15.6%
7 252
11.9%
8 107
 
5.0%
9 69
 
3.2%
10 197
 
9.3%
ValueCountFrequency (%)
10 197
 
9.3%
9 69
 
3.2%
8 107
 
5.0%
7 252
11.9%
6 332
15.6%
5 72
 
3.4%
4 81
 
3.8%
3 53
 
2.5%
2 579
27.2%
1 384
18.1%

Interactions

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2025-05-29T15:00:44.789606image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T15:00:45.851711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T15:00:46.772490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T15:00:47.768023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T15:00:48.764577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T15:00:49.790506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T15:00:50.766706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T15:00:51.839132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T15:00:52.781760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T15:00:53.809439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T15:00:54.816730image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T15:00:55.781055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-29T15:00:56.787657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-05-29T15:01:00.858993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACALTVASTVCLASSDLDPDSFMLBMLTVMSTVMaxMeanMedianMinModeNmaxNzerosTendencyUCVarianceWidth
AC1.000-0.489-0.346-0.244-0.010-0.1190.0000.041-0.105-0.1000.3160.4650.2170.211-0.1650.1840.242-0.0120.1000.1280.3830.347
ALTV-0.4891.0000.4330.074-0.382-0.2170.000-0.0730.335-0.054-0.689-0.3200.2840.2320.4730.218-0.359-0.1630.000-0.282-0.644-0.518
ASTV-0.3460.4331.0000.252-0.1590.0270.0810.2600.312-0.341-0.528-0.1110.1420.1680.2740.135-0.168-0.1880.162-0.223-0.334-0.275
CLASS-0.2440.0740.2521.0000.4440.3000.1870.1470.115-0.2560.0860.114-0.155-0.070-0.206-0.0250.1860.1190.249-0.0750.2870.212
DL-0.010-0.382-0.1590.4441.0000.3240.1940.032-0.172-0.2400.6140.235-0.471-0.323-0.597-0.2420.4730.2830.1040.3030.7090.584
DP-0.119-0.2170.0270.3000.3241.0000.0850.114-0.148-0.2370.3060.112-0.412-0.367-0.310-0.3200.2400.0780.1980.1250.3820.284
DS0.0000.0000.0810.1870.1940.0851.0000.0000.1020.0000.0440.0550.4470.5570.0740.5520.0000.0000.0970.0000.2430.088
FM0.041-0.0730.2600.1470.0320.1140.0001.000-0.026-0.1030.0700.123-0.082-0.042-0.175-0.0290.197-0.1010.103-0.3060.1100.183
LB-0.1050.3350.3120.115-0.172-0.1480.102-0.0261.000-0.065-0.3530.3270.7900.8400.3560.818-0.116-0.0780.270-0.118-0.229-0.143
MLTV-0.100-0.054-0.341-0.256-0.240-0.2370.000-0.103-0.0651.000-0.015-0.0430.058-0.006-0.092-0.0070.0090.0780.094-0.051-0.0920.051
MSTV0.316-0.689-0.5280.0860.6140.3060.0440.070-0.353-0.0151.0000.372-0.459-0.356-0.694-0.3170.5520.2940.0460.3250.7780.714
Max0.465-0.320-0.1110.1140.2350.1120.0550.1230.327-0.0430.3721.0000.3340.412-0.2920.4020.5110.1580.1760.1170.5280.656
Mean0.2170.2840.142-0.155-0.471-0.4120.447-0.0820.7900.058-0.4590.3341.0000.9590.4710.908-0.207-0.1220.291-0.162-0.332-0.232
Median0.2110.2320.168-0.070-0.323-0.3670.557-0.0420.840-0.006-0.3560.4120.9591.0000.3830.962-0.103-0.0830.338-0.130-0.215-0.127
Min-0.1650.4730.274-0.206-0.597-0.3100.074-0.1750.356-0.092-0.694-0.2920.4710.3831.0000.341-0.693-0.3170.214-0.097-0.745-0.904
Mode0.1840.2180.135-0.025-0.242-0.3200.552-0.0290.818-0.007-0.3170.4020.9080.9620.3411.000-0.071-0.0860.368-0.081-0.150-0.094
Nmax0.242-0.359-0.1680.1860.4730.2400.0000.197-0.1160.0090.5520.511-0.207-0.103-0.693-0.0711.0000.2920.1330.1150.6420.776
Nzeros-0.012-0.163-0.1880.1190.2830.0780.000-0.101-0.0780.0780.2940.158-0.122-0.083-0.317-0.0860.2921.0000.0490.0610.3010.323
Tendency0.1000.0000.1620.2490.1040.1980.0970.1030.2700.0940.0460.1760.2910.3380.2140.3680.1330.0491.0000.0780.1500.250
UC0.128-0.282-0.223-0.0750.3030.1250.000-0.306-0.118-0.0510.3250.117-0.162-0.130-0.097-0.0810.1150.0610.0781.0000.2710.132
Variance0.383-0.644-0.3340.2870.7090.3820.2430.110-0.229-0.0920.7780.528-0.332-0.215-0.745-0.1500.6420.3010.1500.2711.0000.827
Width0.347-0.518-0.2750.2120.5840.2840.0880.183-0.1430.0510.7140.656-0.232-0.127-0.904-0.0940.7760.3230.2500.1320.8271.000

Missing values

2025-05-29T15:00:57.785751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-29T15:00:57.875305image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-05-29T15:00:58.083398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

LBACFMUCDLDSDPASTVMSTVALTVMLTVWidthMinMaxNmaxNzerosModeMeanMedianVarianceTendencyCLASS
0120.00.0000.00.0000.0000.00.00073.00.543.02.464.062.0126.02.00.0120.0137.0121.073.0NaN9
1132.00.0060.00.0060.0030.00.00017.02.10.010.4130.068.0198.06.01.0141.0136.0140.012.00.06
2NaN0.0030.00.008NaN0.00.00016.02.10.013.4130.068.0198.05.0NaNNaN135.0138.013.00.06
3134.00.0030.00.0080.003NaN0.00016.02.40.023.0117.053.0170.011.00.0137.0134.0137.013.01.06
4132.00.0070.00.0080.0000.00.00016.02.40.019.9117.053.0170.09.00.0137.0136.0138.011.0NaN2
5134.00.0010.00.0100.0090.00.00226.05.90.00.0150.050.0200.05.03.076.0107.0107.0170.00.08
6134.00.0010.00.0130.008NaN0.00329.06.30.00.0150.050.0200.06.03.071.0107.0106.0215.00.08
7122.00.0000.00.0000.0000.00.00083.00.56.015.668.062.0130.00.00.0122.0122.0123.0NaN1.09
8122.00.0000.00.0020.0000.00.00084.00.55.013.668.062.0130.00.00.0122.0122.0123.0NaN1.09
9122.00.0000.00.0030.0000.00.00086.00.36.010.668.062.0130.01.00.0122.0122.0123.01.01.09
LBACFMUCDLDSDPASTVMSTVALTVMLTVWidthMinMaxNmaxNzerosModeMeanMedianVarianceTendencyCLASS
2116140.00.0040.0000.0040.0000.00.080.00.236.02.218.0140.0158.01.00.0147.0148.0149.01.00.02
2117140.00.0000.0000.0080.000NaN0.079.0NaN20.08.526.0124.0150.01.00.0144.0143.0145.01.01.01
2118140.00.0000.0000.0060.0010.00.079.00.526.07.021.0129.0NaN1.00.0145.0142.0145.02.01.01
2119140.00.0000.0000.007NaN0.0NaN79.00.627.06.426.0124.0150.01.00.0144.0141.0145.01.01.01
2120140.00.0000.0000.0050.0010.00.077.00.717.06.031.0124.0155.02.00.0145.0143.0145.02.00.01
2121140.00.0000.0000.0070.0000.00.0NaN0.225.07.240.0137.0177.04.00.0153.0150.0152.02.00.05
2122140.00.0010.0000.007NaN0.00.078.00.422.07.166.0103.0169.06.00.0152.0148.0151.03.0NaN5
2123140.0NaN0.0000.0070.0000.00.079.00.420.06.167.0103.0170.0NaN0.0153.0148.0152.04.01.05
2124140.00.0010.0000.0060.0000.00.078.00.427.07.066.0103.0169.06.00.0152.0147.0151.04.01.05
2125142.00.0020.0020.0080.0000.00.074.00.436.05.0NaN117.0159.02.01.0145.0143.0145.01.00.01

Duplicate rows

Most frequently occurring

LBACFMUCDLDSDPASTVMSTVALTVMLTVWidthMinMaxNmaxNzerosModeMeanMedianVarianceTendencyCLASS# duplicates
0123.00.0030.0040.0000.00.00.050.00.94.014.882.058.0140.07.00.0129.0128.0130.05.01.022
1148.00.0050.0000.0020.00.00.040.00.90.010.635.0136.0171.01.00.0153.0155.0156.04.00.022